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Digital signal classification using clustering has many applications in the civilian and military domains. Most of the proposed classifiers can only recognize a few types of digital signals. This paper presents a novel technique that deals with the classification of multi-user chirp modulation signals using clustering techniques. In this technique, a combination of higher order moments and cumulants...
A classifier model for satellite image data by using Partitioned-Feature based Classifier (PFC)is proposed in this paper. The PFC does not use concatenated feature vectors extracted from the original data at once to classify each datum, but uses extracted feature vectors to classify data separately. In the training stage, the contribution rate calculated from each feature vector group is drawn throughout...
Atmospheric data sets are represented by an amount of heterogeneous and redundant data. As number of measurements grows, a strategy is needed to select and efficiently analyze the useful information from the whole data set. The aim of this work is to propose a feature extraction technique based on construction of clusters of similar features. The main objective of the proposed process is to attempt...
A web text classification method using a neural network is presented here. The proposed method can classify a set of English text documents into a number of given classes depending on their contents where the number of such classes is not known a priori. Text documents, internet edition of news paper, from various faculties of games and sports are considered for experimentation. The method is found...
Among the large number of genes presented in microarray data, only a small fraction of them are effective for performing a certain diagnostic test. However, it is very difficult to identify these genes for disease diagnosis. In this regard, a new supervised gene clustering algorithm is proposed to cluster genes from microarray data. The proposed method directly incorporates the information of response...
High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of over fitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric...
In document categorization method by using similarity measures based on word vectors, it is important to determine key words to characterize each document. However, conventional methods select the key words based on their frequency or/and particular importance index such as tf-idf. In this paper, we propose a method to characterize each document by using temporal clusters of technical term usages...
In recent years, feature extraction methods make an achievement in pattern recognition and computer vision. It extracts not only useful feature for classification, but also reduces the dimension of pattern samples. In this paper, we propose orthogonal supervised spectral discriminant analysis (OSSDA) which motivated by marginal fisher analysis (MFA) and spectral clustering. It put different weights...
We propose a novel method for fusing different classifiers outputs. Our approach, called Context Extraction for Local Fusion with Fuzzy Integrals (CELF-FI), is a local approach that adapts fuzzy integrals fusion method to different regions of the feature space. It is based on a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective...
Context extraction for local fusion (CELF) is a local approach that combines multiple classifier outputs with the help of feature space information. CELF is based on an objective function that integrates context extraction and decision fusion. Context extraction divides the feature space into homogeneous regions; decision fusion combines multiple classifier outputs in each region or context. Although...
Brain-Computer Interface (BCI) is a system provides an alternative communication and control channel between the human brain and computer. In Motor Imagery-based (MI) BCI system, Common Spatial Pattern (CSP) is frequently used for extracting discriminative patterns from the electroencephalogram (EEG). There are many studies have proven that the performance of CSP has a very important relation with...
Cheap and highly-functional digital cameras are now readily available to the public. In contrast to traditional film-based cameras, tasks of refining, classification and clustering images is burdensome to camera users. Therefore, we need an automated procedure to assist manual management of digital photos. One way to overcome this problem is to provide a summarized view covering the entire set of...
Identification of salient patterns for the classification of gene expression profiles is a useful step in examining the biological significance and correlation of genes with disease states. We propose a clustering-based approach in which feature selection is first carried out to identify influential genes and then salient patterns are determined to characterize each of the different classes. The proposed...
The automatic classification of audio data is an effective way to organize a large-scale audio data files. In this paper, an automatic content-based audio classification model using Centroid Neural Networks (CNN) with a Divergence Measure is proposed. The Divergence-based Centroid Neural Network (DCNN) algorithm, which employs the divergence measure as its distance measure, is used for clustering...
We do research on the problem of moving object classification. Our aim is to classify moving objects of traffic scene videos into pedestrians, bicycles and vehicles. The self-organizing feature map (SOM) is an unsupervised learning algorithm, which is developed by simulating the signal processing of human brain, has the advantage of simple principle and self organization, and has been used in many...
In this paper, a course management system has been designed on the basis of data mining methods such as association rules, classification and clustering. This system aims at analyzing the hidden relationship between the students' academic grades and various data of students' performance in class, and the findings can be used as guidance for better teaching and learning in the future.
The Partitioned Feature-based Classifier (PFC) is proposed in this paper. PFC does not use entire feature vectors extracted from the original data at once to classify each datum, but use only groups of features related to each feature vector to classify data separately. In the training stage, the contribution rate calculated from each feature vector group is drawn throughout the accuracy of each feature...
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these...
We present a novel method for fusing the decisions of multiple classification algorithms which use different features, classification methods, and data sources. The proposed method, called context dependent fusion of multiple algorithms (CDF-MA) is motivated by the fact that the relative performance of different algorithms can vary significantly as the characteristics of the input data vary. The training...
In feature gene selection, filtering model concerns classification accuracy while ignoring gene redundancy problem. On the other hand, gene clustering finds correlated genes without considering their predictive abilities. It is valuable to enhance their performances by the help of each other. We report a new feature gene extraction algorithm, namely double-thresholding extraction of feature gene (DEFG),...
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